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Volume 09 No. 12
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Scientific Investigations

The Accuracy of Eyelid Movement Parameters for Drowsiness Detection

http://dx.doi.org/10.5664/jcsm.3278

Vanessa E. Wilkinson, Ph.D.1; Melinda L. Jackson, Ph.D.1,2; Justine Westlake, B.A./BAppSci (Hons)1; Bronwyn Stevens, BBNSc, PGradDip (Psych)1; Maree Barnes, MB.BS1; Philip Swann, Ph.D.3; Shantha M. W. Rajaratnam, Ph.D.4,5,6; Mark E. Howard, MB.BS., Ph.D.1
1Institute for Breathing & Sleep, Department of Respiratory & Sleep Medicine, Austin Health, Victoria, Australia; 2Melbourne School of Psychological Sciences, The University of Melbourne, Victoria, Australia; 3Department Road Safety, Victoria, Australia; 4School of Psychology and Psychiatry, Monash University, Clayton, Victoria, Australia; 5Division of Sleep Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA; 6Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA

Study Objectives:

Drowsiness is a major risk factor for motor vehicle and occupational accidents. Real-time objective indicators of drowsiness could potentially identify drowsy individuals with the goal of intervening before an accident occurs. Several ocular measures are promising objective indicators of drowsiness; however, there is a lack of studies evaluating their accuracy for detecting behavioral impairment due to drowsiness in real time.

Methods:

In this study, eye movement parameters were measured during vigilance tasks following restricted sleep and in a rested state (n = 33 participants) at three testing points (n = 71 data points) to compare ocular measures to a gold standard measure of drowsiness (OSLER). The utility of these parameters for detecting drowsiness-related errors was evaluated using receiver operating characteristic curves (ROC) (adjusted by clustering for participant) and identification of optimal cutoff levels for identifying frequent drowsiness-related errors (4 missed signals in a minute using OSLER). Their accuracy was tested for detecting increasing frequencies of behavioral lapses on a different task (psychomotor vigilance task [PVT]).

Results:

Ocular variables which measured the average duration of eyelid closure (inter-event duration [IED]) and the ratio of the amplitude to velocity of eyelid closure were reliable indicators of frequent errors (area under the curve for ROC of 0.73 to 0.83, p < 0.05). IED produced a sensitivity and specificity of 71% and 88% for detecting ≥ 3 lapses (PVT) in a minute and 100% and 86% for ≥ 5 lapses. A composite measure of several eye movement characteristics (Johns Drowsiness Scale) provided sensitivities of 77% and 100% for detecting 3 and ≥ 5 lapses in a minute, with specificities of 85% and 83%, respectively.

Conclusions:

Ocular measures, particularly those measuring the average duration of episodes of eye closure are promising real-time indicators of drowsiness.

Citation:

Wilkinson VE; Jackson ML; Westlake J; Stevens B; Barnes M; Swann P; Rajaratnam SMW; Howard ME. The accuracy of eyelid movement parameters for drowsiness detection. J Clin Sleep Med 2013;9(12):1315-1324.




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